methodology

Unlike traditional models that follow a linear trajectory (even if iterative), our AI-Centric Design™ methodology is dynamic, allowing for constant interplay between human insight, artificial intelligence, and technology. This triangular framework ensures that each component informs and enhances the others, creating a system where feedback loops and iterative processes drive innovation.

  • We start with the human element at one vertex—profoundly understanding user needs and contexts. Our process doesn't just begin with humans but continuously loops back to ensure that every AI and technological advancement is grounded in enhancing the human experience.

  • The technology vertex isn't just about the latest gadgets and software—it's about choosing the right tools that serve both the task and the end-user. Our approach cycles through technology choices, constantly refining to ensure our solutions are advanced but also appropriate and sustainable.

  • The 3rd vertex of our methodology is the task concept—understanding, breaking, and rebuilding them better through design and technology. The fluidity of our process means that as tasks evolve, so do our solutions in a symbiotic relationship with AI and human input.

heuristic evaluation framework

Integrating artificial intelligence (AI) in our design processes presents a transformative opportunity to enhance creativity, streamline processes, and deliver exceptional outcomes. AI-Centric Design™, a methodology that leverages AI tools and technologies at every stage of the design process, promises significant improvements in efficiency, innovation, and reliability. However, to fully realize its potential, it is crucial to have a robust framework for evaluating its effectiveness.

This document outlines a comprehensive evaluation framework tailored explicitly for AI-Centric Design™. The goal is to provide design teams with the tools and methods needed to assess their AI-driven projects' performance systematically. By focusing on key performance indicators (KPIs) such as task handling, feedback integration, data utilization, creativity, process iteration, outcome prediction, scalability, error handling, time efficiency, user-centricity, tools and techniques, and outcome reliability, this framework offers a structured approach to measure success and identify areas for improvement.